Diabetes has long been lumped into two categories – Type 1, which often appears in childhood, or Type 2, which is associated with obesity and typically develops later in life. But scientists have learned that not all patients with Type 2 diabetes are the same, with some patients varying in body weight, age of onset and other characteristics.

Now, researchers from Stanford Medicine have developed an artificial intelligence-based algorithm that uses data from continuous blood glucose monitors to parse three of the four most common Type 2 diabetes subtypes.

“It’s a tool that people can use to take preventative measures. If the levels trigger a prediabetes warning, for instance, dietary or exercise habits could be adjusted,” said Michael Snyder, PhD, a professor of genetics who co-led the study. Snyder is the Stanford W. Ascherman, MD, FACS Professor in Genetics.

Roughly 13% of the U.S. population, some 40 million people, have been diagnosed with diabetes, and 98 million have prediabetes, so a widely accessible technology that pinpoints diagnostic details would be a game changer for diabetes care, Snyder said.

“The majority of people with diabetes have Type 2, and they’re just called ‘Type 2,'” said Tracey McLaughlin, MD, a professor of endocrinology. “But it’s more complex than that, and there are different underlying physiologies that lead to the condition.”

There’s been a growing movement to subclassify Type 2 diabetes, which accounts for 95% of all diabetes, to better understand the risk of having other related conditions, such as cardiovascular, kidney, liver or eye complications, and to identify the underlying physiology of individuals’ diabetes. “This matters, because depending on what type you have, some drugs will work better than others,” McLaughlin said. “Our goal was to find a more accessible, on-demand way for people to understand and improve their health.”

The technology could have been useful for Snyder years ago when he learned he was prediabetic. “When I found out I was on my way to becoming diabetic, I increased my muscle mass, which is one of the common ways to help decrease sugar in the blood, but it had no effect. That’s because I’m not traditionally insulin resistant,” he said. His variety of Type 2 diabetes stems from something called a beta cell deficiency, which means that the cells that produce insulin don’t function the way they should.

A paper detailing the research published Dec. 23 in Nature Biomedical Engineering. McLaughlin and Snyder are co-senior authors. Ahmed Metwally, PhD, a former postdoctoral scholar at Stanford Medicine who is now a research scientist at Google, is the lead author.

Delineating details of diabetes

Currently, diagnosing diabetes is based solely on the level of glucose in the blood and can be made through a simple blood draw. “But those tests reveal little about the biology underlying high blood sugar,” McLaughlin said. “Understanding the physiology behind it requires metabolic tests done in a research setting, but the tests are cumbersome and expensive and not practical for use in the clinic.”

However, continuous glucose monitors, available over the counter, can test for high blood sugar and compile more detailed information about their metabolic biology.

Insulin, a hormone made in the pancreas, regulates the levels of glucose, or sugar, in the bloodstream by encouraging cells to absorb it and use it as energy. If the pancreas does not make enough insulin, known as insulin deficiency, blood glucose rises. Insulin resistance, a common marker of diabetes, occurs when cells don’t respond to the cues from insulin, which also results in a buildup of blood glucose.

Type 2 diabetes can also result from a defect in the production of incretin, a hormone released by the gut after eating that stimulates insulin secretion from the pancreas, or by insulin resistance in the liver. Each of these four physiologic subtypes of diabetes might respond to different therapies.

Testing the algorithm

McLaughlin and Snyder wondered whether a common gadget like a continuous glucose monitor could produce data with hidden signals correlating to the different subtypes of diabetes. The device, which users attach to their upper arm, measures the rise and fall of blood sugar levels in real time. People who drink a glucose drink often show a spike in their blood glucose, but the level and pattern of those spikes varies from one person to the next.

In a study of 54 participants, 21 of whom had prediabetes and 33 of whom were healthy, the researchers applied an artificial intelligence-powered algorithm to identify patterns within peaks and dips that corresponded to different subtypes of Type 2 diabetes.

Participants who used the continuous glucose monitors also underwent the oral glucose test performed at a doctor’s office. “People have looked at that for decades and have found certain parameters that indicate insulin resistance or beta cell dysfunction, which are the main drivers of diabetes,” McLaughlin said. “But now we have the monitors, and you can get a much more nuanced picture of the glucose pattern which predicts these subtypes with greater accuracy and can be done at home.”

When compared with clinical data and other biomarkers of metabolic conditions, the algorithm – whether applied to the continuous glucose monitor data or to data from blood draws taken after a glucose tolerance test – predicted metabolic subtypes, such as insulin resistance and beta-cell deficiency, with greater accuracy than the traditional metabolic tests. The tool is able to detect and identify the subtypes correctly about 90% of the time.

Broadening accessibility

In addition to higher-resolution data for people with diabetes or prediabetes, using the monitor has other perks. “Even if a person with insulin resistance doesn’t develop diabetes, it’s still important to know,” McLaughlin said, “because insulin resistance is a risk factor for a variety of other health conditions, like heart disease and fatty-liver disease.”

McLaughlin and Snyder plan to continue testing the algorithm with people who have been diagnosed with Type 2 diabetes and hope that the technology’s broad availability will boost access to care, even when patients aren’t able to make it to a doctor’s appointment.

“We also see this technology as valuable health care tool for people who are economically challenged or geographically isolated and can’t access a health care system,” McLaughlin said.

Stanford’s Department of Genetics and the Department of Medicine supported the work.

This study was funded by The National Institutes of Health (grants R01 DK110186-01, U01-DK105535, U01-DK085545, UM1DK126185 and 2T32HL09804911), the Stanford PHIND center, the Stanford Diabetes Research Center, the Wellcome Trust, Stanford Lifestyle Medicine and the American Diabetes Association.